Novometrics vs. Regression Analysis: Modeling Patient Satisfaction with Care Received in the Emergency Room

Paul R. Yarnold

Optimal Data Analysis, LLC

Ordered dependent (class) variables are ordinarily modeled by Pearson correlation (r) in univariable applications with one ordered independent variable (attribute), and by multiple regression analysis (MRA) in multivariable applications involving more than one attribute. Prior research demonstrated the use of ODA to maximize predictive accuracy of r and MRA models. The present paper demonstrates the use of novometrics, the maximum-accuracy alternative to r and MRA.

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Novometrics vs. Regression Analysis: Literacy, and Age and Income, of Ambulatory Geriatric Patients

Paul R. Yarnold

Optimal Data Analysis, LLC

A convenience sample of 293 ambulatory women patients, all older than 65 years of age, were surveyed in a general medicine clinic. Correlation (r), multiple regression analysis (MRA), and novometric analysis were used to model the relationship of scores (even integers) on the TOFHLA literacy measure (the dependent or class variable) with age (recorded to two significant digits to the right of the decimal) and income (measured as 1 to 8, inclusive, integer annual increments of $10,000). Regression- and novometric-based findings are contrasted.

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Novometric Analysis with Ordered Class Variables: The Optimal Alternative to Linear Regression Analysis

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis, LLC

Employed to model an ordered dependent (class) variable, Pearson correlation (r) is used in univariable applications featuring one ordered independent variable (attribute), and multiple regression analysis (MRA) is utilized in multivariable applications featuring two or more attributes. Prior research demonstrated how to maximize the predictive accuracy of univariable and multivariable regression models vis-à-vis an ODA-based procedure. The present paper instead demonstrates optimal alternatives to r and MRA.

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How Many EO-CTA Models Exist in My Sample and Which is the Best Model?

Paul R. Yarnold

Optimal Data Analysis, LLC

As concerns the existence of statistically reliable enumerated-optimal classification tree analysis (EO-CTA) model(s) for a given application, possible alternative analytic outcomes are: no EO-CTA model exists; one model exists; or a descendant family (DF) that consists of two or more models exists. Models in a DF maximize ESS for unique partitions of the sample, and the model with the lowest observed D statistic is the globally-optimal CTA (GO-CTA) model for the application. The brute-force method of identifying a DF involves obtaining an initial EO-CTA model without specifying minimum end¬point sample size, then applying the minimum denominator selection algorithm (MDSA) to the initial model. A more efficient methodology for obtaining the GO-CTA model involves including only the attribute subset identified using structural decomposition analysis (SDA). The DF for the SDA attribute subset differs from the DF identified for the entire attribute set because the DF is data-specific. These methods are illustrated for an application using rated aspects of nursing and physician care to discriminate 1,045 very satisfied vs. 671 satisfied Emergency Department (ED) patients.

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Pruning CTA Models to Maximize PAC

Paul R. Yarnold

Optimal Data Analysis, LLC

In CTA weighting by prior odds is used if a model is sought to maximize ESS, which is explicitly optimized by a pruning algorithm that deconstructs a fully-grown model into all nested sub-branches and then reassembles all possible combinations of sub-branches to identify the configuration with greatest ESS. In contrast, unit-weighting is used if a CTA model is sought to maximize PAC, explicitly optimized using the pruning algorithm to reassemble all possible sub-branch combinations and identify the configuration with greatest PAC.

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